# jm199504/Financial-Knowledge-Graphs

小型金融知识图谱构建流程（neo4j / python / cypher / KG）

Repository: https://github.com/jm199504/Financial-Knowledge-Graphs
Canonical: https://ross.abutalabs.com/products/financial-knowledge-graphs
Language: Jupyter Notebook
License Family: other
Topics: cypher, data-analysis, graph-database, neo4j, python
Last push: 2024-07-07T12:38:04+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2625, "days_push": 787, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3125, forks 568 (observed 2026-08-28T04:07:44.423958+00:00)

## What it is
A tutorial-style Jupyter Notebook project demonstrating how to build a small financial knowledge graph using Python, Neo4j, and Cypher. It covers data acquisition from free financial APIs, preprocessing, graph construction, visualization queries, and Neo4j graph algorithms like centrality, community detection, and link prediction.

## Use cases
- learn how to build a knowledge graph with neo4j and python
- construct a financial knowledge graph from stock data
- tutorial on cypher queries for graph databases
- run graph algorithms like link prediction and community detection on financial data
- fetch and preprocess stock market data from tushare
- visualize relationships between stocks and shareholders in a graph database

## When to choose
- you want a hands-on, end-to-end walkthrough of building a knowledge graph in the financial domain
- you are learning Neo4j, Cypher, and graph algorithms with real stock market data
- you prefer notebook-based tutorials with explanations in Chinese

## When to avoid
- you need a production-ready financial knowledge graph system or maintained library
- you require a permissively licensed project - this repository has no license
- you need English-language documentation or up-to-date API integrations

## Facets
- artifact type: learning-resource
- maturity: active
- function: database, data-visualization, search-engine, machine-learning
- domain: databases, data-science, tutorials, big-data
- platform: python, cross-platform
- tags: knowledge-graph, neo4j, cypher, graph-database, financial-data, jupyter-notebook, tutorial, chinese-language, finance, docker

## Member repositories
- jm199504/Financial-Knowledge-Graphs (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:44.423958+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:26:23.343644+00:00, confidence not recorded.
  - readme: https://github.com/jm199504/Financial-Knowledge-Graphs (fetched 2026-08-28T04:07:44.423958+00:00, sha 6deccec56304)
- Data as of 2026-08-30T08:39:29.467469+00:00.